Learn Both Concepts & Complex Math Derivations For Neural Networks !

**What you'll learn**

- Step By Step Conceptual Introduction For Neural Networks And Deep Learning [Even If You Are A Beginner]
- Understanding The Basic Perceptron[Neuron] Conceptually, Graphically, And Mathematically - Perceptron Convergence Theorem Proof
- Mathematical Derivations For Deep Learning Modules
- Step-By-Step Derivation Of BackPropagation Algorithm
- Vectorization Of BackPropagation
- Different Performance Metrics Like Performance - Recall - F1 Score - ROC & AUC
- Mathematical Derivation Of Cross-Entropy Cost Function
- Mathematical Derivation Of Back-Propagation Through Batch-Normalization
- Different Solved Examples On Various Topics

**Requirements**

- You Should Be Familiar With College Level Linear Algebra [Advanced]
- You Should Be Familiar With Multi-Variable Calculus And Chain-Rule
- You Should Be Famililar With Basic Probability

**Description**

**Deep Learning**is surely one of the hottest topics nowadays, with a tremendous amount of practical applications in many many fields.Those applications include, without being limited to, image classification, object detection, action recognition in videos, motion synthesis, machine translation, self-driving cars, speech recognition, speech and video generation, natural language processing and understanding, robotics, and many many more.

*Now you might be wondering :*There is a very large number of courses well-explaining deep learning,

**why should I prefer this specific course over them ?**

The answer is :

**You shouldn't**

**!**Most of the other courses heavily focus on "Programming" deep learning applications as fast as possible, without giving detailed explanations on

**the underlying mathematical foundations that the field of deep learning was built upon**. And this is exactly the gap that my course is designed to cover.

**It is designed to be used hand in hand with other programming courses, not to replace them.**

Since this series is

**heavily mathematical,**I will refer many many times during my explanations to sections from my own college level linear algebra course.

**In general, being quite familiar with linear algebra is a real prerequisite for this course.**

Pleasehave a look at the

Please

**course syllables**, and remember :

**This is only part (I) of the deep learning series!**

**Who this course is for:**

- Deep Learning Engineers Or College Students Who Want To Gain Deep Mathematical Understanding Of The Topic